S4: Smoothed Solvers for Soft Tissue Simulation

In this project a new solution framework and simulation toolkit for modelling soft tissue deformations in time-critical biomedical applications will be established, with the aim of bringing computational biomechanics closer to the clinic. An approach based on the smoothed finite element method (SFEM) will be formulated. The work will cover development of both the numerical solution framework and a high performance computation scheme based on graphics processing units (GPUs). The resulting software library will be released to the biomedical community as open source, to promote dissemination and further development.

Computational biomechanics provides a powerful basis for modelling soft tissues in biomedical applications. In this project, solid biomechanics problems are of interest: analysis and simulation of the motion and mechanical response of deformable solid tissues. Herein, continuum mechanics formalism provides the mathematical basis for analysis, generally formulated as a set of partial differential equations, and the finite element (FE) method is easily the predominant solution approach. With such a framework, one can, in principle, compute the deformations and stresses produced in arbitrarily complicated structures, under the influence of arbitrarily complicated loads. This capability is of central importance in biomechanics. It is also a key enabling technology for initiatives like the Virtual Physiological Human (www.vph-noe.eu) and the Physiome Project (http://physiomeproject.org), which aim, ultimately, to realise the vision of in silico medicine based on personalised computational modelling. These simulation technologies are also essential tools in development of systems for guidance and planning of highly localised and minimally invasive therapies, and in interactive simulators, for example for risk-free surgeon training.

This project is motivated by three key difficulties that inhibit integration of FE-based models of this kind into clinical applications: (i) model construction: FE methods require discretisation of the involved structures into a high quality mesh of "well shaped" elements, which process remains labour intensive and time consuming for complicated biological structures, and which is particularly detrimental when patient-specific models are required; (ii) handling of large deformations: even after a carefully constructed, high quality mesh has been produced, the large deformations that soft tissues may undergo can distort the mesh so much that the solution fails nonetheless; and (iii) computation time: FE methods are computationally intensive, rendering them unsuitable for time-critical applications like surgical guidance and interactive simulators, or limiting the resolution of large scale simulations like bone microstructural models. SFEMs, the focus of developments in this project, are a recent innovation in computational mechanics, which arise from "smoothing" of spatial gradient fields (e.g. strains) over subdomains of the FE mesh. Among other favourable properties, existing formulations are known to reduce mesh sensitivity substantially. In this project, this feature will form the starting point for an algorithm for which meshes are easier to construct in the first place, and which is insensitive to large deformations, subsequently. The approach will also be explicitly formulated to maximise its efficiency in execution on parallel hardware, thus allowing substantial acceleration using cheap and efficient GPUs. By these means, the proposed simulation framework potentially will ameliorate all three of the mentioned difficulties, thus promoting integration of computational biomechanics into clinically-relevant applications.

S4 will promote national competitiveness in the medical technologies sector. Despite ongoing economic pressure, the UK MedTech sector has defied the recession and experienced continuous growth: according to Department of Business, Innovation and Skills data released in February 2014, sector turnover and employment grew at equivalents (compounded annually) of 2% and 5%, respectively, between 2009 and 2012; in 2012, the sector turned over £17.6bn and employed 76,700 people (the largest sector employer in the UK Life Sciences industry). Globally, the sector is worth £223bn. Given the problems of aging population and concomitant healthcare needs, the market is likely to see even faster growth in future, and the UK's MedTech research base must be supported, if the UK economy is properly to benefit. Simulation already is a central tool in medical device design and its importance will increase as it is further incorporated into clinical workflows (e.g. for surgical planning and guidance) and, in the longer term, as in silico medicine technologies are realised. S4 will support the MedTech industry by overcoming several key challenges of developing patient-specific tissue- and organ-scale simulations. S4's simulation tools will improve soft tissue modelling reliability, significantly accelerate the simulation process itself, and help to automate the process of individualised model generation. Concrete application examples are prosthesis design, for which faster and more accurate assessment of device performance and interactions with tissues will be possible; interactive surgical simulators, in which higher fidelity models of tissue response and motion will be feasible; and surgical planning systems, for which faster and more automated surgical outcomes predictions may be leveraged.

Ultimately, it is the clinical community that drives applications in these areas, and it is they and the patients they serve who will benefit in the medium to longer term. As well as through more immediate improvements in medical devices and systems, improved simulation will benefit clinicians and patients of the future by promoting realisation of the in silico medicine paradigm. The recently published "Roadmap for the Digital Patient" (http://www.digital-patient.net/) sets out a strategy for achievement and widespread adoption of such technologies. These will allow individualisation of clinical decision-making by systematically integrating patient data and personalised physiological models to predict, for example, disease progression and responses to different therapies. From a patient (and, correspondingly, wider societal health) point of view, this is a revolutionary change from the current population-based approach to medicine. At the core of the vision are reliable and efficient patient-specific simulation technologies. Via the methodological advances described above (viz. reliability, acceleration and automation), S4 will support faster translation of simulation-based methods into clinical practice and, in turn, realisation of personalised in silico medicine.

If adopted, S4's outputs will also impact upstream technology developers, such as finite element analysis (FEA) software vendors and GPU manufacturers. The former may achieve competitive advantages by using S4 tools to improve the performance of their software, while the latter would benefit from further penetration of their devices into healthcare and simulation markets. As mentioned previously, the methods developed within S4 certainly have applications in general (non-biomedical) engineering analysis, also, meaning that the impact for the FEA and GPU industries could be significant.